Data Engineer

Most of what makes American healthcare expensive isnt medical care. Its the machinery wrapped around it: middlemen taking a cut fraud nobody stops and billing systems designed to fight over payment instead of deliver care. The result is higher premiums denied claims surprise bills and a system patients increasingly experience as adversarial.

Arlo is rebuilding health insurance for small businesses from first principles: making sure as much of every premium dollar as possible goes to care instead of getting absorbed by the system around it. We do that by identifying fraud earlier steering members toward higher-quality and lower-cost care automating operational overhead and eliminating vendors whose business exists mostly to take a cut.

AI is the foundation that makes this work. We use it across underwriting operations clinical programs and member experience to build an insurer that becomes more efficient as the technology improves.

Were already operating at meaningful scale: profitable hundreds of millions in premiums tens of thousands of members covered and growing quickly through brokers employers and partners. Backed by Upfront Ventures 8VC and General Catalyst with a team from Palantir YC companies and longtime healthcare operators.

The Opportunity

Arlo quotes small businesses using AI-powered underwriting and the quality of that underwriting is only as good as the data beneath it. Were hiring a Data Engineer to build and maintain the pipelines models and monitoring systems that keep our data infrastructure clean timely and trustworthy.

This is a hands-on individual contributor role. Youll sit at the boundary between data engineering and data science working directly with underwriting pricing and analytics teams to ensure the right data reaches the right systems at the right time.

What Youll Work On

Pipeline development and maintenance

• Build and maintain ingestion pipelines for complex heterogeneous data sources TPA feeds carrier data census files claims eligibility and enrollment records

• Design and implement dbt models and transformation logic that produce clean reliable source of truth tables used across underwriting pricing and reporting

• Own pipeline orchestration using tools like Dagster or Airflow ensuring reliable scheduling retries and alerting

Data quality and observability

• Build monitoring and alerting for data inconsistencies: duplicate records mismatched member IDs enrollment timing gaps and carrier reporting lags

• Profile ingest delay characteristics across live policy data and flag where structural latency introduces systematic bias

• Maintain clear documentation of known data quality limitations so downstream teams know what the data can and cannot reliably support

Collaboration with data science

• Partner closely with the data science team to build and maintain feature pipelines that feed underwriting and pricing models

• Support feedback loop infrastructure that carries post-quoting learnings back into upstream models

• Work with engineering to prioritize data quality fixes and accelerate resolution of upstream issues

What Were Looking For

• 35 years in a data engineering or backend engineering role with significant data pipeline ownership

• Proficiency in Python and SQL; comfortable writing production-quality code in both

• Hands-on experience with pipeline orchestration tools (Dagster Airflow Prefect or similar)

• Experience with dbt or equivalent transformation frameworks

• Familiarity with cloud data environments (AWS GCP or Azure) and columnar/analytical databases

• Track record working with messy real-world datasets and building systems that handle inconsistency gracefully

• Strong instincts around data quality you catch problems before they reach downstream consumers

Nice to have

• Background in health insurance claims data or actuarial/TPA data environments

• Experience supporting ML feature pipelines or working alongside data science teams

• Familiarity with MLflow or similar MLOps tooling

• Exposure to healthcare data standards or sensitive regulated data environments

How Youll Work

Youll own your projects end-to-end from initial scoping through to production deployment and ongoing monitoring. Theres no separate ML engineering handoff; youll work directly with the people who depend on your pipelines daily. The role requires equal comfort in Python-based engineering and SQL-driven analysis and a genuine interest in understanding the business context behind the data.

Interview Process

1. Intro call with our recruiter

2. Resume interview with an Arlo co-founder

3. Technical take-home challenge (data engineering problem)

4. Onsite (or virtual): technical review behavioral/cultural interviews

Compensation

$180000 - $220000 equity

• High ownership: Youll get real responsibility from day oneour high-trust team empowers you to run with big problems and shape core parts of the company.

• Join an important mission: Your work directly influences how people access care and improves lives at scale.

• Growth & expansion: Were moving fast and as we grow your scope will grow with usnew challenges bigger opportunities and rapid career velocity.

• Apply AI to a problem that matters: Instead of optimizing ads or cutting labor costs youll use AI to fundamentally reimagine how people get healthcare.

• High pace high collaboration: We operate with velocity first-principles thinking and a team that works closely openly and with ambition.

Exact compensation inclusive of salary and any bonuses is determined based on a number of factors including experience and skill level location and qualifications which are assessed during the interview process.

Arlo is an equal opportunity employer. We do not discriminate based on age race color creed or religion national origin sexual orientation gender identity or expression military status sex disability predisposing genetic characteristics marital status familial status status as a victim of domestic violence or arrest or conviction record as defined under New York State law.

Your safety matters to us. If youre selected to move forward in our hiring process youll hear directly from a member of our Recruiting team via an @ email address. We will never ask for personal or financial information outside of our formal onboarding process. When in doubt please reach out to us to verify at: .

Required Experience:

IC